Stroke Subtype Classification Research Articles

Risk Factors and Etiological Classification of Ischemic Stroke Subtypes in Southwest Iran

Precise categorization of the causes of ischemic stroke (IS) is crucial for optimizing stroke treatment and assessing the prognosis of patients. This cross-sectional study aimed to determine the risk factors and various subcategories of IS in Iranian patients. The study included all patients with IS referred to Golestan Hospital (Ahvaz, Iran) for one year. Their demographics and clinical data were collected. The etiology of IS was classified based on the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) criteria. A total of 1100 patients with IS were evaluated, 658 were male and 442 were female. They had an average age of 66 years (ranging between 20 and 99 years). The majority of them were in the 61-80 age group. The prevalence of risk factors for IS included hypertension (HTN) (71.4%), diabetes (50.4%), smoking (42.4%), history of previous stroke (28%), dyslipidemia (15.4%), and cardiovascular disease (22.5%). Three months after admission, the mortality rate was 10.7% and the majority of patients exhibited a lower level of disability based on the modified Rankin Score (mRS) compared to the time of admission. The frequency of all risk factors, except for HTN, differed significantly between genders (P<0.05). Furthermore, the prevalence of risk factors varied significantly among different stroke etiologic subgroups (P<0.05). The most common etiological factors identified by TOAST classification were associated with large artery atherosclerosis (LAA) and small artery occlusion (SAO). Significant variations were observed in the prevalence of different etiologic subtypes of stroke among genders and across different age groups.

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.

Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (EV-miRNAs) to distinguish the following ischemic stroke subtypes: large artery atherosclerosis (LAA), cardioembolic stroke (CES), and small artery occlusion (SAO). Using next-generation sequencing (NGS) and machine-learning techniques, we identified differentially expressed miRNAs (DEMs) associated with each subtype. Through patient selection and diagnostic evaluation, a cohort of 70 patients with acute ischemic stroke was classified: 24 in the LAA group, 24 in the SAO group, and 22 in the CES group. Our findings revealed distinct EV-miRNA profiles among the groups, suggesting their potential as diagnostic markers. Machine-learning models, particularly logistic regression models, exhibited a high diagnostic accuracy of 92% for subtype discrimination. The collective influence of multiple miRNAs was more crucial than that of individual miRNAs. Additionally, bioinformatics analyses have elucidated the functional implications of DEMs in stroke pathophysiology, offering insights into the underlying mechanisms. Despite limitations like sample size constraints and retrospective design, our study underscores the promise of EV-miRNAs coupled with machine learning for ischemic stroke subtype classification. Further investigations are warranted to validate the clinical utility of the identified EV-miRNA biomarkers in stroke patients.

A Multi-Parametric Approach for Characterising Cerebral Haemodynamics in Acute Ischaemic and Haemorrhagic Stroke.

Early differentiation between acute ischaemic (AIS) and haemorrhagic stroke (ICH), based on cerebral and peripheral hemodynamic parameters, would be advantageous to allow for pre-hospital interventions. In this preliminary study, we explored the potential of multiple parameters, including dynamic cerebral autoregulation, for phenotyping and differentiating each stroke sub-type. Eighty patients were included with clinical stroke syndromes confirmed by computed tomography within 48 h of symptom onset. Continuous recordings of bilateral cerebral blood velocity (transcranial Doppler ultrasound), end-tidal CO2 (capnography), electrocardiogram (ECG), and arterial blood pressure (ABP, Finometer) were used to derive 67 cerebral and peripheral parameters. A total of 68 patients with AIS (mean age 66.8 ± SD 12.4 years) and 12 patients with ICH (67.8 ± 16.2 years) were included. The median ± SD NIHSS of the cohort was 5 ± 4.6. Statistically significant differences between AIS and ICH were observed for (i) an autoregulation index (ARI) that was higher in the unaffected hemisphere (UH) for ICH compared to AIS (5.9 ± 1.7 vs. 4.9 ± 1.8 p = 0.07); (ii) coherence function for both hemispheres in different frequency bands (AH, p < 0.01; UH p < 0.02); (iii) a baroreceptor sensitivity (BRS) for the low-frequency (LF) bands that was higher for AIS (6.7 ± 4.2 vs. 4.10 ± 2.13 ms/mmHg, p = 0.04) compared to ICH, and that the mean gain of the BRS in the LF range was higher in the AIS than in the ICH (5.8 ± 5.3 vs. 2.7 ± 1.8 ms/mmHg, p = 0.0005); (iv) Systolic and diastolic velocities of the affected hemisphere (AH) that were significantly higher in ICH than in AIS (82.5 ± 28.09 vs. 61.9 ± 18.9 cm/s), systolic velocity (p = 0.002), and diastolic velocity (p = 0.05). Further multivariate modelling might improve the ability of multiple parameters to discriminate between AIS and ICH and warrants future prospective studies of ultra-early classification (<4 h post symptom onset) of stroke sub-types.

TOAST stroke subtype classification in clinical practice: implications for the Get With The Guidelines-Stroke nationwide registry.

The TOAST (Trial of ORG 10172 in Acute Stroke Treatment) is the most commonly used ischemic stroke subtype classification system worldwide and a required field in the US National Get With The Guidelines-Stroke (GWTG-Stroke) registry. However, stroke diagnostics have advanced substantially since the TOAST classification was designed 30 years ago, potentially making it difficult to apply reliably. In this prospective diagnostic accuracy study, we analyzed consecutive ischemic stroke patients admitted to a Comprehensive Stroke Center between July-October 2021. Clinical practice TOAST classification diagnoses rendered by the stroke team in the electronic medical record (EMR) at discharge were retrieved from GWTG-Stroke registry and compared to a reference ("gold") standard diagnosis derived from agreement between two expert raters after review of the EMR and patient imaging. Among 49 patients; age was 72.3 years (±12.1), 53% female, and presenting NIHSS median 3 (IQR 1-11). Work-up included: brain imaging in 100%; cardiac rhythm assessment in 100%; cervical/cerebral vessel imaging in 98%; TTE ± TEE in 92%; and TCD emboli evaluation in 51%. Reference standard diagnoses were: LAA-6%, SVD-14%, CE-39%, OTH-10%, UND-M (more than one cause)-20%, and UND-C (cryptogenic)-10%. GWTG-Stroke TOAST diagnoses agreed with reference standard diagnoses in 30/49 (61%). Among the 6 subtype diagnoses, specificity was generally high (84.8%-97.7%), but sensitivity suboptimal for LAA (33%), OTH (60%), UND-M (10%), and UND-C (20%). Positive predictive value was suboptimal for 5 of the 6 subtypes: LAA (13%), SVD (58%), OTH (75%), UND-M (50%), and UND-C (50%). Clinical practice TOAST classification subtype diagnoses entered into the GWTG-Stroke registry were accurate in only 61% of patients, a performance rate that, if similarly present at other centers, would hamper the ability of the national registry to provide dependable insights into subtype-related care. Development of an updated ischemic stroke subtype classification system, with algorithmic logic embedded in electronic medical records, is desirable.

Considering Context-Specific microRNAs in Ischemic Stroke with Three "W": Where, When, and What.

MicroRNAs are short non-coding RNA molecules that function as critical regulators of various biological processes through negative regulation of gene expression post-transcriptionally. Recent studies have indicated that microRNAs are potential biomarkers for ischemic stroke. In this review, we first illustrate the pathogenesis of ischemic stroke and demonstrate the biogenesis and transportation of microRNAs from cells. We then discuss several promising microRNA biomarkers in ischemic stroke in a context-specific manner from three dimensions: biofluids selection for microRNA extraction (Where), the timing of sample collection after ischemic stroke onset (When), and the clinical application of the differential-expressed microRNAs during stroke pathophysiology (What). We show that microRNAs have the utilities in ischemic stroke diagnosis, risk stratification, subtype classification, prognosis prediction, and treatment response monitoring. However, there are also obstacles in microRNA biomarker research, and this review will discuss the possible ways to improve microRNA biomarkers. Overall, microRNAs have the potential to assist clinical treatment, and developing microRNA panels for clinical application is worthwhile.

Seasonal Variations in Stroke and a Comparison of the Predictors of Unfavorable Outcomes among Patients with Acute Ischemic Stroke and Cardioembolic Stroke.

We investigated the seasonal variations in stroke in 4040 retrospectively enrolled patients with acute ischemic stroke (AIS) admitted between January 2011 and December 2022, particularly those with cardioembolic (CE) stroke, and compared predictors of unfavorable outcomes between AIS patients and CE stroke patients. The classification of stroke subtypes was based on the Trial of ORG 10172 in Acute Stroke Treatment. Stroke occurrence was stratified by seasons and weekdays or holidays. Of all AIS cases, 18% were of CE stroke. Of all five ischemic stroke subtypes, CE stroke patients were the oldest; received the most thrombolysis and thrombectomy; had the highest initial National Institutes of Stroke Scale (NIHSS) and discharge modified Rankin Scale (mRS) scores; and had the highest rate of in-hospital complications, unfavorable outcomes (mRS > 2), and mortality. The highest CE stroke prevalence was noted in patients aged ≥ 85 years (30.9%); moreover, CE stroke prevalence increased from 14.9% in summer to 23.0% in winter. The main predictors of death in patients with CE stroke were age > 86 years, heart rate > 79 beats/min, initial NIHSS score > 16, neutrophil-to-lymphocyte ratio (NLR) > 6.4, glucose > 159 mg/dL, cancer history, in-hospital complications, and neurological deterioration (ND). The three most dominant factors influencing death, noted in not only patients with AIS but also those with CE stroke, are high initial NIHSS score, ND, and high NLR. We selected the most significant factors to establish nomograms for predicting fatal outcomes. Effective heart rhythm monitoring, particularly in older patients and during winter, may help develop stroke prevention strategies and facilitate early AF detection.

Carotid stenosis and cryptogenic stroke

ObjectivesCryptogenic stroke represents a type of ischemic stroke with an unknown origin, presenting a significant challenge in both stroke management and prevention. According to the Trial of Org 10,172 in Acute Stroke Treatment criteria, a stroke is categorized as being caused by large artery atherosclerosis only when there is >50% luminal narrowing of the ipsilateral internal carotid artery. However, nonstenosing carotid artery plaques can be an underlying cause of ischemic stroke. Indeed, emerging evidence documents that some features of plaque vulnerability may act as an independent risk factor, regardless of the degree of stenosis, in precipitating cerebrovascular events. This review, drawing from an array of imaging-based studies, explores the predictive values of carotid imaging modalities in the detection of nonstenosing carotid plaque (<50%), that could be the cause of a cerebrovascular event when some features of vulnerability are present. MethodsGoogle Scholar, Scopus, and PubMed were searched for articles on cryptogenic stroke and those reporting the association between cryptogenic stroke and imaging features of carotid plaque vulnerability. ResultsDespite extensive diagnostic evaluations, the etiology of a considerable proportion of strokes remains undetermined, contributing to the recurrence rate and persistent morbidity in affected individuals. Advances in imaging modalities, such as magnetic resonance imaging, computed tomography scans, and ultrasound examination, facilitate more accurate detection of nonstenosing carotid artery plaque and allow better stratification of stroke risk, leading to a more tailored treatment strategy. ConclusionsEarly detection of nonstenosing carotid plaque with features of vulnerability through carotid imaging techniques impacts the clinical management of cryptogenic stroke, resulting in refined stroke subtype classification and improved patient management. Additional research is required to validate these findings and recommend the integration of these state-of-the-art imaging methodologies into standard diagnostic protocols to improve stroke management and prevention.

Deep learning for prediction of mechanism in acute ischemic stroke using brain diffusion magnetic resonance image

Background: Acute ischemic stroke is a disease with multiple etiologies. Therefore, identifying the mechanism of acute ischemic stroke is fundamental to its treatment and secondary prevention. The Trial of Org 10172 in Acute Stroke Treatment classification is currently the most widely used system, but it often has a limitations of classifying unknown causes and inadequate inter-rater reliability. Therefore, we attempted to develop a three-dimensional (3D)-convolutional neural network (CNN)-based algorithm for stroke lesion segmentation and subtype classification using only the diffusion and apparent diffusion coefficient information of patients with acute ischemic stroke. Methods: This study included 2,251 patients with acute ischemic stroke who visited our hospital between February 2013 and July 2019. Results: The segmentation model for lesion segmentation in the training set achieved a Dice score of 0.843±0.009. The subtype classification model achieved an average accuracy of 81.9%, with accuracies of 81.6% for large artery atherosclerosis, 86.8% for cardioembolism, 72.9% for small vessel occlusion, and 86.3% for control.Conclusion: We developed a model to predict the mechanism of cerebral infarction using diffusion magnetic resonance imaging, which has great potential for identifying diffusion lesion segmentation and stroke subtype classification. As deep learning systems are gradually developing, they are becoming useful in clinical practice and applications.

Plasma D-dimer as a biomarker for the early classification of common acute ischemic stroke subtypes in Indonesia

BackgroundD-dimer is a well-known marker for abnormal hemostasis in acute ischemic stroke (AIS), indicating the presence of fibrin degradation due to thrombus formation and lysis. The diagnostic performance of D-dimer for different AIS types in the Indonesian population has not been established. The aim of this study is to compare the plasma D-dimer levels in three of the most common AIS subtypes in Indonesia; the cardioembolic, large artery atherosclerosis (LAA), and small-vessel occlusion (SVO), and to determine its most optimal diagnostic performance.ResultsIn this cross-sectional study, 64 subjects with confirmed AIS diagnosis at the Wahidin Sudirohusodo General Hospital Makassar between June and October 2019 were recruited. Plasma D-dimer levels were measured and grouped according to the subtype of acute ischemic stroke based on the TOAST classification. A significant difference was observed between the D-dimer levels across the three AIS subtypes, with an average D-dimer of 2.93 ± 1.7, 1.27 ± 0.81, and 0.56 ± 0.46 µg/ml in the cardioembolic, LAA, and SVO subtypes, respectively. As a marker of cardioembolic stroke, an optimal cut-off was determined to be 1.52 µg/ml, yielding a sensitivity of 84.44% (CI 71.22–92.25% and specificity of 84.21% (CI 62.43–94.48%).ConclusionPlasma D-dimer levels varied significantly between the cardioembolic, LAA, and SVO subtypes of AIS, with the highest D-dimer level in the cardioembolic subtypes. As a marker of cardioembolic stroke, an optimal cut-off was determined to be 1.52 µg/mL, yielding a sensitivity and specificity of 84.44% and 84.21%, respectively.

The value of serum Lp-PLA2 combined with MPO in the diagnosis of cerebral infarction caused by large artery atherosclerosis

ObjectiveTo explore the value of serum lipoprotein-associated phospholipase A2（Lp-PLA2）combined with myeloperoxidase（MPO）for the diagnosis of large artery atherosclerosis(LAA) cerebral infarction. MethodsBaseline data were collected from patients with first-ever acute cerebral infarction, serum Lp-PLA2 and MPO levels were measured. The etiology of cerebral infarction was classified according to the Chinese Ischemic Stroke Subtype Classification Standard. The risk factors associated with LAA cerebral infarction were identified by univariate and multivariate regression analysis. The diagnostic value of serum Lp-PLA2 and MPO for LAA cerebral infarction was assessed by the area under the receiver-operating characteristic (ROC) curve. ResultsOverall 368 patients were involved, 148 patients (40.22 %) were LAA. The serum La-PLA2 and MPO levels were higher in the LAA group than those in non-LAA group (23.06 ± 3.39 ng/mL versus 17.48 ± 3.26 ng/mL; 93.60 ± 9.58 ng/mL versus 75.98 ± 15.53 ng/mL; P < 0.001 for both). Multivariate analysis showed that elevated levels of serum Lp-PLA2 (OR 1.742, 95 %CI 1.499–2.025; P < 0.001) and MPO (OR 1.060, 95 % CI 1.026–1.096; P = 0.001) were the independent risk factors of LAA cerebral infarction. The area under curve of the serum Lp-PLA2 combined with MPO for the diagnosis of LAA cerebral infarction was 0.896 [0.866 ∼ 0.927] (P < 0.001). ConclusionSerum Lp-PLA2 combined with MPO could be valued as a predictor of acute cerebral infarction caused by large artery atherosclerosis.

Abstract TMP49: Accuracy Of Toast Subtype Classification In Clinical Practice: Implications For The Get With The Guidelines National Stroke Registry

Background: TOAST is the most commonly used ischemic stroke subtype classification system worldwide and a required field in the US National GWTG-Stroke registry. However, stroke diagnostics have advanced substantially since TOAST was designed 30 years ago, potentially making it difficult to now apply reliably. Methods: We analyzed consecutive ischemic stroke patients admitted to a CSC between July-Oct 2021. Clinical practice TOAST diagnoses rendered by the stroke team in the EMR at discharge were retrieved from GWTG-Stroke registry and compared to a reference (“gold”) standard diagnosis derived from agreement between 2 expert raters after review of the EMR and patient imaging. Results: Among 49 patients, age was 72.3y (±12.1), 53% female, and presenting NIHSS median 3 (IQR 1-11). Work-up included: brain imaging in 100%; cardiac rhythm assessment in 100%; cervical/cerebral vessel imaging in 98%; TTE±TEE in 92%; and TCD emboli eval in 51%. Reference standard diagnoses were: LAA-6%, SVD-14%, CE-41%, OTH-8%, UND-more than one cause-18%, and UND-cryptogenic-12%. The Figure shows a bubble plot of congruence between EMR/GWTG-Stroke and reference standard diagnoses. GWTG-Stroke TOAST diagnoses agreed with reference standard diagnoses in 29/49 (59%). Among the 6 subtype diagnoses singly, specificity was generally high (83%-100%), but sensitivity suboptimal for LAA (33%), OTH (75%), UND-MT1 (0%), and UND-CRYPT (17%). Positive predictive value was suboptimal for all: LAA (13%), SVD (58%), CE (77%), OTH (75%), SVD OTH (75%), UND-MT1 (0%), and UND-CRYPT (33%). Conclusion: Clinical practice subtype diagnoses using TOAST and entered into GWTG-Stroke were accurate in only 59% of patients, hampering the ability of the national registry to provide dependable insights into subtype-related aspects of care. Development of an updated ischemic stroke subtype classification system, with algorithmic logic directly embedded in electronic medical records, is desirable.

Features of pathogenesis and risk factors of in-hospital ischemic stroke

In-hospital ischemic stroke (IHIS) is one of the most critical competing diseases that can develop in patients during inpatient treatment. Causes associated with diagnostic and therapeutic measures may play an additional role in the pathogenesis of IHIS, in addition to traditional risk factors. Objective : to study the pathogenesis features and assess risk factors for IHIS. Patients and methods . The comorbidity, main and additional risk factors of IHIS in patients of therapeutic (n=112; 44.3%), surgical (n=125; 49.4%) and neurological (n=16; 6.3%) profiles were studied. Results and discussion . The proportion of perioperative stroke in the study was 37.2%. The frequency of atherothrombotic, cardioembolic, and lacunar subtypes of IHIS did not differ in groups of patients of different profiles. In the group of surgical patients, the proportion of strokes of another established etiology was 24.8% and was higher than in therapeutic (9.8%; p&lt;0.05) and neurological (6.25%; p&lt;0.005) patients. The proportion of strokes of unknown etiology in the group of surgical patients, on the contrary, was lower (15.2%) than in patients with therapeutic (35.7%; p&lt;0.05) and neurological (50.0%; p&lt;0.05) profile. An additional risk factor for IHIS for non-surgical patients was insufficient antithrombotic prophylaxis, for surgical patients – surgery. Conclusion . Compared with out-of-hospital ischemic stroke, the causes of in-hospital ischemic stroke are more diverse and cannot always be explained solely by the higher comorbidity of hospitalized patients. The TOAST classification of ischemic stroke subtypes does not fully reflect the pathogenesis of IHIS, as it does not take into account the possibility of external influences, especially in cases where it is not possible to establish the exact cause of stroke, and the most likely trigger is medical intervention.

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis.

BackgroundStroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as early as possible for disease prevention. Furthermore, retinal characteristics have been associated with stroke and can be used for stroke risk estimation. This study investigated machine learning approaches to retinal images for risk estimation and classification of ischemic and haemorrhagic stroke.Study designA case-control study was conducted in the Shenzhen Traditional Chinese Medicine Hospital. According to the computerized tomography scan (CT) or magnetic resonance imaging (MRI) results, stroke patients were classified as either ischemic or hemorrhage stroke. In addition, a control group was formed using non-stroke patients from the hospital and healthy individuals from the community. Baseline demographic and medical information was collected from participants' hospital medical records. Retinal images of both eyes of each participant were taken within 2 weeks of admission. Classification models using a machine-learning approach were developed. A 10-fold cross-validation method was used to validate the results.Results711 patients were included, with 145 ischemic stroke patients, 86 haemorrhagic stroke patients, and 480 controls. Based on 10-fold cross-validation, the ischemic stroke risk estimation has a sensitivity and a specificity of 91.0% and 94.8%, respectively. The area under the ROC curve for ischemic stroke is 0.929 (95% CI 0.900 to 0.958). The haemorrhagic stroke risk estimation has a sensitivity and a specificity of 93.0% and 97.1%, respectively. The area under the ROC curve is 0.951 (95% CI 0.918 to 0.983).ConclusionA fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.

Interpretable CNN for ischemic stroke subtype classification with active model adaptation

BackgroundTOAST subtype classification is important for diagnosis and research of ischemic stroke. Limited by experience of neurologist and time-consuming manual adjudication, it is a big challenge to finish TOAST classification effectively. We propose a novel active deep learning architecture to classify TOAST.MethodsTo simulate the diagnosis process of neurologists, we drop the valueless features by XGB algorithm and rank the remaining ones. Utilizing active learning framework, we propose a novel causal CNN, in which it combines with a mixed active selection criterion to optimize the uncertainty of samples adaptively. Meanwhile, KL-focal loss derived from the enhancement of Focal loss by KL regularization is introduced to accelerate the iterative fine-tuning of the model.ResultsTo evaluate the proposed method, we construct a dataset which consists of totally 2310 patients. In a series of sequential experiments, we verify the effectiveness of each contribution by different evaluation metrics. Experimental results show that the proposed method achieves competitive results on each evaluation metric. In this task, the improvement of AUC is the most obvious, reaching 77.4.ConclusionsWe construct a backbone causal CNN to simulate the neurologist process of that could enhance the internal interpretability. The research on clinical data also indicates the potential application value of this model in stroke medicine. Future work we would consider various data types and more comprehensive patient types to achieve fully automated subtype classification.

Women and Stroke: Different, yet Similar

Background: The impact of gender on acute ischemic stroke, in terms of presentation, severity, etiology, and outcome, is increasingly getting recognized. Here, we analyzed the gender-related differences in etiology and outcome of ischemic stroke in South India. Methods: Patients with first ever ischemic stroke within 1 week of onset presenting to the Comprehensive Stroke Care Centre, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India, were included in our study. Clinical and risk factor profile was documented. The stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS) at onset, and stroke subtype classification was done using Trial of Org 10172 in Acute Ischemic Stroke criteria. The 3-month functional outcome was assessed using the modified Rankin Scale (mRS) with excellent outcome defined as an mRS ≤2. Results: Of the 742 patients, 250 (33.7%) were females. The age, clinical profile, and rate of reperfusion therapies did not differ between the genders. Women suffered more severe strokes (mean NIHSS 9.5 vs. 8.4, p = 0.03). While large artery atherosclerosis was more common in men (21.3% vs. 14.8%, p = 0.03), cardioembolic strokes secondary to rheumatic heart disease were more common in women (27.2% vs. 19.7%, p = 0.02). Men had a better 3-month functional outcome compared to women (68.6% vs. 61.2%, p = 0.04), but was not statistically significant after adjusting for confounders. Conclusion: Our data, from a single comprehensive stroke unit from South India, suggest that stroke in women are different, yet similar in many ways to men. Guideline-based treatment can result in comparable short-term outcomes, irrespective of admission stroke severity.

Risk factors among stroke subtypes and its impact on the clinical outcome of patients of Northern Portugal under previous aspirin therapy

BackgroundIn Western European countries, acute ischemic stroke (AIS) remains the third leading cause of death. Among the risk factors for cerebrovascular disease, some have more influence than others in certain stroke subtypes. The aim of this study was to evaluate the impact of risk factors among Stroke Subtypes on the clinical outcome of Portuguese patients under previous aspirin therapy. Materials and methodsWe studied a cohort of 371 patients diagnosed with AIS and a clinical follow-up protocol was set up.The patients were admitted in a Department of Internal Medicine of a major hospital. Standardized data assessment and stroke subtype classification (Oxfordshire Community Stroke Project) were used. ResultsArterial hypertension (80.4 %), overweight (72.6 %) and dyslipidemia (62.0 %) were the most prevalent risk factors with no statistical differences among the group’s subtypes. Current smoking was more prevalent in POCI(62.9 %) with differences among subtypes (p = 0.002). Atrial fibrillation was more commonly reported in TACI (39.3 %) and less common in POCI (8.1 %) (p < 0.001).Comparing TACI vs Non TACI Stroke Subtypes demonstrated major differences in cumulative survival,among the cases with no previous aspirin treatment, after 3 years (51.9 % vs 88.8 %).The increased risk of mortality at 12 months is consistently observed for the presence of a previous atrial fibrillation (OR 3.01 95 %CI 1.69−5.39), TACI subtype (OR 10.4 95 %CI 4.83−22.6) and NIHSS over 10 (OR 9.33 95 % CI 4,49−19.4). When we analyze the impact of previous aspirin treatment in the risk for a new stroke event, it seems to have a protective effect in a time frame of 12 months, but this protection is lost extending at 24 months (p = 0.094 vs p = 0.005). DiscussionOur results indicate that smoking, atrial fibrillation and age have different relevance in their distribution among ischemic stroke subtypes at the time of diagnosis. Concerning the influence of the main stroke risk factors on the clinical outcome, our results present a strong influence of atrial fibrillation and of age. Severity of disease at diagnosis, represented by TACI subtype is clearly associated to decreased survival among patients with no record of previous aspirin therapy. Our results reinforce the relevance cohort studies of different populations, to achieve a more comprehensive knowledge of the impact of risk factors on stroke subtypes and on its clinical outcome.

Automated Electronic Phenotyping of Cardioembolic Stroke.

Oral anticoagulation is generally indicated for cardioembolic strokes, but not for other stroke causes. Consequently, subtype classification of ischemic stroke is important for risk stratification and secondary prevention. Because manual classification of ischemic stroke is time-intensive, we assessed the accuracy of automated algorithms for performing cardioembolic stroke subtyping using an electronic health record (EHR) database. We adapted TOAST (Trial of ORG 10172 in Acute Stroke Treatment) features associated with cardioembolic stroke for derivation in the EHR. Using administrative codes and echocardiographic reports within Mass General Brigham Biobank (N=13 079), we iteratively developed EHR-based algorithms to define the TOAST cardioembolic stroke features, revising regular expression algorithms until achieving positive predictive value ≥80%. We compared several machine learning-based statistical algorithms for discriminating cardioembolic stroke using the feature algorithms applied to EHR data from 1598 patients with acute ischemic strokes from the Massachusetts General Hospital Ischemic Stroke Registry (2002-2010) with previously adjudicated TOAST and Causative Classification of Stroke subtypes. Regular expression-based feature extraction algorithms achieved a mean positive predictive value of 95% (range, 88%-100%) across 11 echocardiographic features. Among 1598 patients from the Massachusetts General Hospital Ischemic Stroke Registry, 1068 had any cardioembolic stroke feature within predefined time windows in proximity to the stroke event. Cardioembolic stroke tended to occur at an older age, with more TOAST-based comorbidities, and with atrial fibrillation (82.3%). The best model was a random forest with 92.2% accuracy and area under the receiver operating characteristic curve of 91.1% (95% CI, 87.5%-93.9%). Atrial fibrillation, age, dilated cardiomyopathy, congestive heart failure, patent foramen ovale, mitral annulus calcification, and recent myocardial infarction were the most discriminatory features. Machine learning-based identification of cardioembolic stroke using EHR data is feasible. Future work is needed to improve the accuracy of automated cardioembolic stroke identification and assess generalizability of electronic phenotyping algorithms across clinical settings.

Fractal and multifractional-based predictive optimization model for stroke subtypes’ classification

Numerous natural phenomena display repeating self-similar patterns. Fractal is used when a pattern seems to repeat itself. Fractal and multifractal methods have extensive applications in neurosciences in which the prevalence of fractal properties like self-similarity in the brain, equipped with a complex structure, in medical data analysis at various levels of observation is admitted. The methods come to the fore since subtle details are not always detected by physicians, but these are critical particularly in neurological diseases like stroke which may be life-threatening. The aim of this paper is to identify the self-similar, significant and efficient attributes to achieve high classification accuracy rates for stroke subtypes. Accordingly, two approaches were implemented. The first approach is concerned with application of the fractal and multifractal methods on the stroke dataset in order to identify the regular, self-similar, efficient and significant attributes from the dataset, with these steps: a) application of Box-counting dimension generated BC_stroke dataset b) application of Wavelet transform modulus maxima generated WTMM_stroke dataset. The second approach involves the application of Feed Forward Back Propagation (FFBP) for stroke subtype classification with these steps: (i) FFBP algorithm was applied on the stroke dataset, BC_stroke dataset and WTMM_stroke dataset. (ii) Comparative analyses were performed based on accuracy, sensitivity and specificity for the three datasets. The main contribution is that the study has obtained the identification of self-similar, regular and significant attributes from the stroke subtypes datasets by following multifarious and integrated methodology. The study methodology is based on the singularity spectrum which provides a value concerning how fractal a set of points are in the datasets (BC_stroke dataset and WTMM_stroke dataset). The experimental results reveal the applicability, reliability and accuracy of our proposed integrated method. No earlier work exists in the literature with the relevant stroke datasets and the methods employed. Therefore, the study aims at pointing a new direction in the relevant fields concerning the complex dynamic systems and structures which display multifractional nature.

Serum alpha-1 antitrypsin in acute ischemic stroke: A prospective pilot study

BackgroundAlpha-1 antitrypsin (AAT) is a potent anti-protease enzyme which may play a role in arterial wall stability. A variant of its encoding gene has been recently linked to ischemic stroke due to large artery atherosclerosis (LAA). We sought to explore potential relationships between ischemic stroke mechanisms, atherosclerosis burden and serum AAT levels. MethodsWe performed a prospective observational study of consecutive patients with acute ischemic stroke who were admitted to an academic comprehensive stroke center over a three-month period. Blood samples were collected within 24 h of hospital admission, and stroke subtype classification was determined based on modified TOAST criteria. Modified Woodcock scoring system was used to quantify calcification of major cervico-cranial arteries as a surrogate for atherosclerosis burden. Linear regression analysis was used to assess the association between serum AAT levels and calcification scores, both as continuous variables. ResultsAmong eighteen patients met our inclusion criteria and were enrolled in our study, 10 patients (56%) were men; mean age was 66 (SD 12.5); median NIH stroke scale was 4 (IQR 9.5); 8 patients (44%) had stroke due to LAA. The median serum level of AAT was 140 mg/dl (IQR 41.7) for patients with LAA-related stroke, and 148.5 mg/dl (IQR 37.7) for patients with other stroke mechanisms (p = 0.26). Higher serum AAT levels was associated with lower modified Woodcock calcification scores. (p-value = 0.038) ConclusionsMeasurement of AAT levels in patients with acute stroke is feasible, and there may be associations between AAT levels and stroke mechanism that warrant further study in larger samples.

Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke.

Background and Purpose- Classification of stroke as cardioembolic in etiology can be challenging, particularly since the predominant cause, atrial fibrillation (AF), may not be present at the time of stroke. Efficient tools that discriminate cardioembolic from noncardioembolic strokes may improve care as anticoagulation is frequently indicated after cardioembolism. We sought to assess and quantify the discriminative power of AF risk as a classifier for cardioembolism in a real-world population of patients with acute ischemic stroke. Methods- We performed a cross-sectional analysis of a multi-institutional sample of patients with acute ischemic stroke. We systematically adjudicated stroke subtype and examined associations between AF risk using CHA2DS2-VASc, Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score, and the recently developed Electronic Health Record-Based AF score, and cardioembolic stroke using logistic regression. We compared the ability of AF risk to discriminate cardioembolism by calculating C statistics and sensitivity/specificity cutoffs for cardioembolic stroke. Results- Of 1431 individuals with ischemic stroke (age, 65±15; 40% women), 323 (22.6%) had cardioembolism. AF risk was significantly associated with cardioembolism (CHA2DS2-VASc: odds ratio [OR] per SD, 1.69 [95% CI, 1.49-1.93]; Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score: OR, 2.22 [95% CI, 1.90-2.60]; electronic Health Record-Based AF: OR, 2.55 [95% CI, 2.16-3.04]). Discrimination was greater for Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score (C index, 0.695 [95% CI, 0.663-0.726]) and Electronic Health Record-Based AF score (0.713 [95% CI, 0.681-0.744]) versus CHA2DS2-VASc (C index, 0.651 [95% CI, 0.619-0.683]). Examination of AF scores across a range of thresholds indicated that AF risk may facilitate identification of individuals at low likelihood of cardioembolism (eg, negative likelihood ratios for Electronic Health Record-Based AF score ranged 0.31-0.10 at sensitivity thresholds 0.90-0.99). Conclusions- AF risk scores associate with cardioembolic stroke and exhibit moderate discrimination. Utilization of AF risk scores at the time of stroke may be most useful for identifying individuals at low probability of cardioembolism. Future analyses are warranted to assess whether stroke subtype classification can be enhanced to improve outcomes in undifferentiated stroke.